Overview

Dataset statistics

Number of variables14
Number of observations20000
Missing cells42841
Missing cells (%)15.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory104.0 B

Variable types

Numeric8
Categorical5
Boolean1

Warnings

car_type has a high cardinality: 373 distinct values High cardinality
average_price is highly correlated with priceHigh correlation
price is highly correlated with average_price and 1 other fieldsHigh correlation
diff_price is highly correlated with price and 1 other fieldsHigh correlation
diff_price_perc is highly correlated with diff_priceHigh correlation
average_price is highly correlated with priceHigh correlation
price is highly correlated with average_price and 1 other fieldsHigh correlation
diff_price is highly correlated with price and 1 other fieldsHigh correlation
diff_price_perc is highly correlated with diff_priceHigh correlation
average_price is highly correlated with priceHigh correlation
price is highly correlated with average_price and 1 other fieldsHigh correlation
diff_price is highly correlated with price and 1 other fieldsHigh correlation
diff_price_perc is highly correlated with diff_priceHigh correlation
antiquity is highly correlated with card_brandHigh correlation
price is highly correlated with card_brand and 1 other fieldsHigh correlation
card_brand is highly correlated with antiquity and 3 other fieldsHigh correlation
average_price is highly correlated with price and 1 other fieldsHigh correlation
km is highly correlated with card_brandHigh correlation
DI has 14280 (71.4%) missing values Missing
price has 14280 (71.4%) missing values Missing
sold has 14280 (71.4%) missing values Missing
car_id is uniformly distributed Uniform
antiquity has 447 (2.2%) zeros Zeros

Reproduction

Analysis started2021-05-29 22:35:05.943004
Analysis finished2021-05-29 22:40:20.643353
Duration5 minutes and 14.7 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

car_id
Real number (ℝ≥0)

UNIFORM

Distinct19999
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean9999.498575
Minimum0
Maximum19999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-05-29T17:40:20.749733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999.9
Q14999.5
median9999
Q314999.5
95-th percentile18999.1
Maximum19999
Range19999
Interquartile range (IQR)10000

Descriptive statistics

Standard deviation5773.791378
Coefficient of variation (CV)0.5774080905
Kurtosis-1.200090018
Mean9999.498575
Median Absolute Deviation (MAD)5000
Skewness7.405197625 × 10-7
Sum199979972
Variance33336666.88
MonotonicityNot monotonic
2021-05-29T17:40:20.889698image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
8201
 
< 0.1%
75921
 
< 0.1%
108421
 
< 0.1%
137621
 
< 0.1%
48711
 
< 0.1%
183561
 
< 0.1%
51081
 
< 0.1%
2621
 
< 0.1%
142301
 
< 0.1%
Other values (19989)19989
99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
199991
< 0.1%
199981
< 0.1%
199971
< 0.1%
199961
< 0.1%
199951
< 0.1%
199941
< 0.1%
199931
< 0.1%
199921
< 0.1%
199911
< 0.1%
199901
< 0.1%

car_type
Categorical

HIGH CARDINALITY

Distinct373
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Serie 1
 
180
Ram 1500 V6
 
171
Odyssey
 
153
Transit
 
149
Saveiro
 
148
Other values (368)
19199 

Length

Max length25
Median length6
Mean length6.88445
Min length2

Characters and Unicode

Total characters137689
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSupra
2nd rowSupra
3rd rowSupra
4th rowSupra
5th rowSupra

Common Values

ValueCountFrequency (%)
Serie 1180
 
0.9%
Ram 1500 V6171
 
0.9%
Odyssey153
 
0.8%
Transit149
 
0.7%
Saveiro148
 
0.7%
Terrain147
 
0.7%
CR-V145
 
0.7%
March144
 
0.7%
Serie 7141
 
0.7%
Civic140
 
0.7%
Other values (363)18482
92.4%

Length

2021-05-29T17:40:21.107784image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
clase1022
 
3.7%
serie749
 
2.7%
van368
 
1.3%
ram368
 
1.3%
cargo364
 
1.3%
quattro336
 
1.2%
range306
 
1.1%
rover306
 
1.1%
v6306
 
1.1%
1500284
 
1.0%
Other values (380)23059
83.9%

Most occurring characters

ValueCountFrequency (%)
a12832
 
9.3%
e11185
 
8.1%
r10843
 
7.9%
7547
 
5.5%
o6787
 
4.9%
i6251
 
4.5%
n5753
 
4.2%
t5438
 
3.9%
C4808
 
3.5%
s4396
 
3.2%
Other values (55)61849
44.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter87438
63.5%
Uppercase Letter30354
 
22.0%
Decimal Number11751
 
8.5%
Space Separator7547
 
5.5%
Dash Punctuation599
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a12832
14.7%
e11185
12.8%
r10843
12.4%
o6787
 
7.8%
i6251
 
7.1%
n5753
 
6.6%
t5438
 
6.2%
s4396
 
5.0%
l3310
 
3.8%
u2671
 
3.1%
Other values (17)17972
20.6%
Uppercase Letter
ValueCountFrequency (%)
C4808
15.8%
S4181
13.8%
R2322
 
7.6%
M1899
 
6.3%
T1846
 
6.1%
V1620
 
5.3%
A1590
 
5.2%
L1543
 
5.1%
X1542
 
5.1%
E1368
 
4.5%
Other values (16)7635
25.2%
Decimal Number
ValueCountFrequency (%)
03902
33.2%
51610
13.7%
31136
 
9.7%
1963
 
8.2%
6893
 
7.6%
4890
 
7.6%
8854
 
7.3%
2772
 
6.6%
7616
 
5.2%
9115
 
1.0%
Space Separator
ValueCountFrequency (%)
7547
100.0%
Dash Punctuation
ValueCountFrequency (%)
-599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin117792
85.5%
Common19897
 
14.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a12832
 
10.9%
e11185
 
9.5%
r10843
 
9.2%
o6787
 
5.8%
i6251
 
5.3%
n5753
 
4.9%
t5438
 
4.6%
C4808
 
4.1%
s4396
 
3.7%
S4181
 
3.5%
Other values (43)45318
38.5%
Common
ValueCountFrequency (%)
7547
37.9%
03902
19.6%
51610
 
8.1%
31136
 
5.7%
1963
 
4.8%
6893
 
4.5%
4890
 
4.5%
8854
 
4.3%
2772
 
3.9%
7616
 
3.1%
Other values (2)714
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII137663
> 99.9%
Latin 1 Sup26
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a12832
 
9.3%
e11185
 
8.1%
r10843
 
7.9%
7547
 
5.5%
o6787
 
4.9%
i6251
 
4.5%
n5753
 
4.2%
t5438
 
4.0%
C4808
 
3.5%
s4396
 
3.2%
Other values (54)61823
44.9%
Latin 1 Sup
ValueCountFrequency (%)
ó26
100.0%

color
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Black
6991 
White
5048 
Red
4043 
Blue
3315 
Orange
 
603

Length

Max length6
Median length5
Mean length4.4601
Min length3

Characters and Unicode

Total characters89202
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlue
2nd rowBlack
3rd rowBlack
4th rowBlue
5th rowBlack

Common Values

ValueCountFrequency (%)
Black6991
35.0%
White5048
25.2%
Red4043
20.2%
Blue3315
16.6%
Orange603
 
3.0%

Length

2021-05-29T17:40:21.281458image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-29T17:40:21.342566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
black6991
35.0%
white5048
25.2%
red4043
20.2%
blue3315
16.6%
orange603
 
3.0%

Most occurring characters

ValueCountFrequency (%)
e13009
14.6%
B10306
11.6%
l10306
11.6%
a7594
8.5%
c6991
7.8%
k6991
7.8%
W5048
 
5.7%
h5048
 
5.7%
i5048
 
5.7%
t5048
 
5.7%
Other values (7)13813
15.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter69202
77.6%
Uppercase Letter20000
 
22.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e13009
18.8%
l10306
14.9%
a7594
11.0%
c6991
10.1%
k6991
10.1%
h5048
 
7.3%
i5048
 
7.3%
t5048
 
7.3%
d4043
 
5.8%
u3315
 
4.8%
Other values (3)1809
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
B10306
51.5%
W5048
25.2%
R4043
 
20.2%
O603
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin89202
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e13009
14.6%
B10306
11.6%
l10306
11.6%
a7594
8.5%
c6991
7.8%
k6991
7.8%
W5048
 
5.7%
h5048
 
5.7%
i5048
 
5.7%
t5048
 
5.7%
Other values (7)13813
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII89202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e13009
14.6%
B10306
11.6%
l10306
11.6%
a7594
8.5%
c6991
7.8%
k6991
7.8%
W5048
 
5.7%
h5048
 
5.7%
i5048
 
5.7%
t5048
 
5.7%
Other values (7)13813
15.5%

km
Real number (ℝ≥0)

HIGH CORRELATION

Distinct19993
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25259639.99
Minimum1394070
Maximum49452737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-05-29T17:40:21.423521image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1394070
5-th percentile10611032.15
Q118958897.75
median25294503
Q331499937.5
95-th percentile40072602.05
Maximum49452737
Range48058667
Interquartile range (IQR)12541039.75

Descriptive statistics

Standard deviation8917901.232
Coefficient of variation (CV)0.3530494194
Kurtosis-0.4508520235
Mean25259639.99
Median Absolute Deviation (MAD)6269228.5
Skewness0.02030405366
Sum5.051927997 × 1011
Variance7.952896238 × 1013
MonotonicityNot monotonic
2021-05-29T17:40:21.539165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
220086802
 
< 0.1%
211512232
 
< 0.1%
356753272
 
< 0.1%
188044422
 
< 0.1%
301795282
 
< 0.1%
261477152
 
< 0.1%
283612312
 
< 0.1%
133768411
 
< 0.1%
302646591
 
< 0.1%
267564341
 
< 0.1%
Other values (19983)19983
99.9%
ValueCountFrequency (%)
13940701
< 0.1%
16204261
< 0.1%
16461431
< 0.1%
16608071
< 0.1%
17085901
< 0.1%
18182471
< 0.1%
18596421
< 0.1%
18903641
< 0.1%
18919431
< 0.1%
20451341
< 0.1%
ValueCountFrequency (%)
494527371
< 0.1%
494162011
< 0.1%
494042851
< 0.1%
490611301
< 0.1%
488225021
< 0.1%
487072641
< 0.1%
486957281
< 0.1%
486485191
< 0.1%
486170541
< 0.1%
485704261
< 0.1%

average_price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct700
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean551491.0049
Minimum101729
Maximum997705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-05-29T17:40:21.658934image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum101729
5-th percentile149481
Q1330625
median559715
Q3781472
95-th percentile951729
Maximum997705
Range895976
Interquartile range (IQR)450847

Descriptive statistics

Standard deviation257113.0326
Coefficient of variation (CV)0.4662143721
Kurtosis-1.209466392
Mean551491.0049
Median Absolute Deviation (MAD)226560
Skewness-0.02910769406
Sum1.10298201 × 1010
Variance6.610711154 × 1010
MonotonicityNot monotonic
2021-05-29T17:40:21.767557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30781248
 
0.2%
23818444
 
0.2%
46992343
 
0.2%
13342043
 
0.2%
12190942
 
0.2%
59839541
 
0.2%
99706740
 
0.2%
19637240
 
0.2%
82025140
 
0.2%
20739140
 
0.2%
Other values (690)19579
97.9%
ValueCountFrequency (%)
10172934
0.2%
10248622
0.1%
10613229
0.1%
10622730
0.1%
10758324
0.1%
10805632
0.2%
11264322
0.1%
11727728
0.1%
12098529
0.1%
12190942
0.2%
ValueCountFrequency (%)
99770521
0.1%
99710428
0.1%
99706740
0.2%
99348125
0.1%
99217926
0.1%
99216729
0.1%
99087817
0.1%
98896823
0.1%
98671132
0.2%
98607833
0.2%

transmission
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Automatic
15874 
Manual
4126 

Length

Max length9
Median length9
Mean length8.3811
Min length6

Characters and Unicode

Total characters167622
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowManual

Common Values

ValueCountFrequency (%)
Automatic15874
79.4%
Manual4126
 
20.6%

Length

2021-05-29T17:40:21.952785image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-29T17:40:22.010529image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
automatic15874
79.4%
manual4126
 
20.6%

Most occurring characters

ValueCountFrequency (%)
t31748
18.9%
a24126
14.4%
u20000
11.9%
A15874
9.5%
o15874
9.5%
m15874
9.5%
i15874
9.5%
c15874
9.5%
M4126
 
2.5%
n4126
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter147622
88.1%
Uppercase Letter20000
 
11.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t31748
21.5%
a24126
16.3%
u20000
13.5%
o15874
10.8%
m15874
10.8%
i15874
10.8%
c15874
10.8%
n4126
 
2.8%
l4126
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
A15874
79.4%
M4126
 
20.6%

Most occurring scripts

ValueCountFrequency (%)
Latin167622
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t31748
18.9%
a24126
14.4%
u20000
11.9%
A15874
9.5%
o15874
9.5%
m15874
9.5%
i15874
9.5%
c15874
9.5%
M4126
 
2.5%
n4126
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII167622
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t31748
18.9%
a24126
14.4%
u20000
11.9%
A15874
9.5%
o15874
9.5%
m15874
9.5%
i15874
9.5%
c15874
9.5%
M4126
 
2.5%
n4126
 
2.5%

body_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Sedan
11780 
SUV
4943 
Coupe
2371 
Truck
 
906

Length

Max length5
Median length5
Mean length4.5057
Min length3

Characters and Unicode

Total characters90114
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSedan
2nd rowSedan
3rd rowSedan
4th rowSedan
5th rowSedan

Common Values

ValueCountFrequency (%)
Sedan11780
58.9%
SUV4943
24.7%
Coupe2371
 
11.9%
Truck906
 
4.5%

Length

2021-05-29T17:40:22.158145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-29T17:40:22.221334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
sedan11780
58.9%
suv4943
24.7%
coupe2371
 
11.9%
truck906
 
4.5%

Most occurring characters

ValueCountFrequency (%)
S16723
18.6%
e14151
15.7%
d11780
13.1%
a11780
13.1%
n11780
13.1%
U4943
 
5.5%
V4943
 
5.5%
u3277
 
3.6%
C2371
 
2.6%
o2371
 
2.6%
Other values (5)5995
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60228
66.8%
Uppercase Letter29886
33.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e14151
23.5%
d11780
19.6%
a11780
19.6%
n11780
19.6%
u3277
 
5.4%
o2371
 
3.9%
p2371
 
3.9%
r906
 
1.5%
c906
 
1.5%
k906
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
S16723
56.0%
U4943
 
16.5%
V4943
 
16.5%
C2371
 
7.9%
T906
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin90114
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S16723
18.6%
e14151
15.7%
d11780
13.1%
a11780
13.1%
n11780
13.1%
U4943
 
5.5%
V4943
 
5.5%
u3277
 
3.6%
C2371
 
2.6%
o2371
 
2.6%
Other values (5)5995
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII90114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S16723
18.6%
e14151
15.7%
d11780
13.1%
a11780
13.1%
n11780
13.1%
U4943
 
5.5%
V4943
 
5.5%
u3277
 
3.6%
C2371
 
2.6%
o2371
 
2.6%
Other values (5)5995
 
6.7%

DI
Real number (ℝ≥0)

MISSING

Distinct99
Distinct (%)1.7%
Missing14280
Missing (%)71.4%
Infinite0
Infinite (%)0.0%
Mean22.91468531
Minimum1
Maximum131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-05-29T17:40:22.297139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median20
Q334
95-th percentile55.05
Maximum131
Range130
Interquartile range (IQR)25

Descriptive statistics

Standard deviation17.35294604
Coefficient of variation (CV)0.7572849376
Kurtosis1.26195219
Mean22.91468531
Median Absolute Deviation (MAD)12
Skewness1.013808371
Sum131072
Variance301.1247362
MonotonicityNot monotonic
2021-05-29T17:40:22.405799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1199
 
1.0%
2198
 
1.0%
3193
 
1.0%
4184
 
0.9%
5166
 
0.8%
8158
 
0.8%
7158
 
0.8%
6156
 
0.8%
10149
 
0.7%
13140
 
0.7%
Other values (89)4019
 
20.1%
(Missing)14280
71.4%
ValueCountFrequency (%)
1199
1.0%
2198
1.0%
3193
1.0%
4184
0.9%
5166
0.8%
6156
0.8%
7158
0.8%
8158
0.8%
9137
0.7%
10149
0.7%
ValueCountFrequency (%)
1311
 
< 0.1%
1151
 
< 0.1%
1131
 
< 0.1%
1111
 
< 0.1%
1072
< 0.1%
1051
 
< 0.1%
1011
 
< 0.1%
991
 
< 0.1%
961
 
< 0.1%
953
< 0.1%

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5703
Distinct (%)99.7%
Missing14280
Missing (%)71.4%
Infinite0
Infinite (%)0.0%
Mean598836.383
Minimum98972
Maximum1163319
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-05-29T17:40:22.511054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum98972
5-th percentile158602.25
Q1348538.5
median612722.5
Q3841746.5
95-th percentile1030949.35
Maximum1163319
Range1064347
Interquartile range (IQR)493208

Descriptive statistics

Standard deviation280196.5412
Coefficient of variation (CV)0.4679016659
Kurtosis-1.157495585
Mean598836.383
Median Absolute Deviation (MAD)244683.5
Skewness-0.02344815849
Sum3425344111
Variance7.85101017 × 1010
MonotonicityNot monotonic
2021-05-29T17:40:22.636271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5411572
 
< 0.1%
7560742
 
< 0.1%
3304522
 
< 0.1%
3016042
 
< 0.1%
8788392
 
< 0.1%
11266032
 
< 0.1%
4319242
 
< 0.1%
5900432
 
< 0.1%
6019712
 
< 0.1%
3323082
 
< 0.1%
Other values (5693)5700
 
28.5%
(Missing)14280
71.4%
ValueCountFrequency (%)
989721
< 0.1%
1041341
< 0.1%
1044701
< 0.1%
1045671
< 0.1%
1047921
< 0.1%
1049011
< 0.1%
1052141
< 0.1%
1069351
< 0.1%
1076011
< 0.1%
1081371
< 0.1%
ValueCountFrequency (%)
11633191
< 0.1%
11606981
< 0.1%
11576421
< 0.1%
11572261
< 0.1%
11553581
< 0.1%
11550091
< 0.1%
11542961
< 0.1%
11517891
< 0.1%
11510261
< 0.1%
11472081
< 0.1%

sold
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing14280
Missing (%)71.4%
Memory size156.4 KiB
True
4738 
False
 
982
(Missing)
14280 
ValueCountFrequency (%)
True4738
 
23.7%
False982
 
4.9%
(Missing)14280
71.4%
2021-05-29T17:40:22.709702image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

diff_price
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5577
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-721226593.8
Minimum-1010101010
Maximum11625
Zeros0
Zeros (%)0.0%
Negative19970
Negative (%)99.9%
Memory size156.4 KiB
2021-05-29T17:40:22.782236image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-1010101010
5-th percentile-1010101010
Q1-1010101010
median-1010101010
Q3-101063.5
95-th percentile-14205.35
Maximum11625
Range1010112635
Interquartile range (IQR)1009999946

Descriptive statistics

Standard deviation456442450.4
Coefficient of variation (CV)-0.6328696895
Kurtosis-1.102911981
Mean-721226593.8
Median Absolute Deviation (MAD)0
Skewness0.9472055271
Sum-1.442453188 × 1013
Variance2.083397106 × 1017
MonotonicityNot monotonic
2021-05-29T17:40:22.892527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-101010101014280
71.4%
-118913
 
< 0.1%
-461363
 
< 0.1%
-427563
 
< 0.1%
-205863
 
< 0.1%
-100762
 
< 0.1%
-247532
 
< 0.1%
-418172
 
< 0.1%
-876822
 
< 0.1%
-784922
 
< 0.1%
Other values (5567)5698
 
28.5%
ValueCountFrequency (%)
-101010101014280
71.4%
-1724411
 
< 0.1%
-1717801
 
< 0.1%
-1715771
 
< 0.1%
-1704541
 
< 0.1%
-1668851
 
< 0.1%
-1667591
 
< 0.1%
-1664491
 
< 0.1%
-1661881
 
< 0.1%
-1654631
 
< 0.1%
ValueCountFrequency (%)
116251
< 0.1%
103141
< 0.1%
86111
< 0.1%
45851
< 0.1%
37881
< 0.1%
28071
< 0.1%
27801
< 0.1%
25801
< 0.1%
25441
< 0.1%
23301
< 0.1%

diff_price_perc
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5720
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-721212121.2
Minimum-1010101010
Maximum0.08004052685
Zeros0
Zeros (%)0.0%
Negative19970
Negative (%)99.9%
Memory size156.4 KiB
2021-05-29T17:40:23.000959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-1010101010
5-th percentile-1010101010
Q1-1010101010
median-1010101010
Q3-0.1536200889
95-th percentile-0.03649030825
Maximum0.08004052685
Range1010101010
Interquartile range (IQR)1010101010

Descriptive statistics

Standard deviation456465317.7
Coefficient of variation (CV)-0.6329140961
Kurtosis-1.102912003
Mean-721212121.2
Median Absolute Deviation (MAD)0
Skewness0.9472055205
Sum-1.442424242 × 1013
Variance2.083605863 × 1017
MonotonicityNot monotonic
2021-05-29T17:40:23.113033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-101010101014280
71.4%
-0.13882626572
 
< 0.1%
-0.1120234071
 
< 0.1%
-0.056418110861
 
< 0.1%
-0.022056504441
 
< 0.1%
-0.068562986481
 
< 0.1%
-0.15049641291
 
< 0.1%
-0.03560242191
 
< 0.1%
-0.052281068061
 
< 0.1%
-0.16047107071
 
< 0.1%
Other values (5710)5710
 
28.5%
ValueCountFrequency (%)
-101010101014280
71.4%
-0.1799951941
 
< 0.1%
-0.17955231031
 
< 0.1%
-0.1793545431
 
< 0.1%
-0.17908413691
 
< 0.1%
-0.1787118641
 
< 0.1%
-0.17863778491
 
< 0.1%
-0.17846050231
 
< 0.1%
-0.1784311251
 
< 0.1%
-0.17842838321
 
< 0.1%
ValueCountFrequency (%)
0.080040526851
< 0.1%
0.051466291241
< 0.1%
0.051101906541
< 0.1%
0.030474167751
< 0.1%
0.022716913081
< 0.1%
0.016876498331
< 0.1%
0.01338250071
< 0.1%
0.012939177931
< 0.1%
0.012782908561
< 0.1%
0.011711821781
< 0.1%

card_brand
Categorical

HIGH CORRELATION

Distinct47
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Nissan
1413 
Mercedes Benz
1412 
Chevrolet
 
1316
Ford
 
1288
Volkswagen
 
1189
Other values (42)
13382 

Length

Max length13
Median length6
Mean length6.46825
Min length3

Characters and Unicode

Total characters129365
Distinct characters43
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowToyota
2nd rowToyota
3rd rowToyota
4th rowToyota
5th rowToyota

Common Values

ValueCountFrequency (%)
Nissan1413
 
7.1%
Mercedes Benz1412
 
7.1%
Chevrolet1316
 
6.6%
Ford1288
 
6.4%
Volkswagen1189
 
5.9%
Audi1167
 
5.8%
Dodge1131
 
5.7%
BMW990
 
5.0%
Honda968
 
4.8%
Toyota935
 
4.7%
Other values (37)8191
41.0%

Length

2021-05-29T17:40:23.328024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nissan1413
 
6.4%
benz1412
 
6.4%
mercedes1412
 
6.4%
chevrolet1316
 
6.0%
ford1288
 
5.9%
volkswagen1189
 
5.4%
audi1167
 
5.3%
dodge1131
 
5.2%
bmw990
 
4.5%
honda968
 
4.4%
Other values (38)9651
44.0%

Most occurring characters

ValueCountFrequency (%)
e15072
 
11.7%
o10715
 
8.3%
a9097
 
7.0%
n7672
 
5.9%
d7184
 
5.6%
s6901
 
5.3%
r6161
 
4.8%
i6099
 
4.7%
t4894
 
3.8%
u4778
 
3.7%
Other values (33)50792
39.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter101996
78.8%
Uppercase Letter25399
 
19.6%
Space Separator1970
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e15072
14.8%
o10715
10.5%
a9097
 
8.9%
n7672
 
7.5%
d7184
 
7.0%
s6901
 
6.8%
r6161
 
6.0%
i6099
 
6.0%
t4894
 
4.8%
u4778
 
4.7%
Other values (13)23423
23.0%
Uppercase Letter
ValueCountFrequency (%)
M3702
14.6%
B2730
 
10.7%
C2275
 
9.0%
A1734
 
6.8%
V1610
 
6.3%
F1455
 
5.7%
N1413
 
5.6%
H1273
 
5.0%
R1136
 
4.5%
D1131
 
4.5%
Other values (9)6940
27.3%
Space Separator
ValueCountFrequency (%)
1970
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin127395
98.5%
Common1970
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e15072
 
11.8%
o10715
 
8.4%
a9097
 
7.1%
n7672
 
6.0%
d7184
 
5.6%
s6901
 
5.4%
r6161
 
4.8%
i6099
 
4.8%
t4894
 
3.8%
u4778
 
3.8%
Other values (32)48822
38.3%
Common
ValueCountFrequency (%)
1970
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII129365
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e15072
 
11.7%
o10715
 
8.3%
a9097
 
7.0%
n7672
 
5.9%
d7184
 
5.6%
s6901
 
5.3%
r6161
 
4.8%
i6099
 
4.7%
t4894
 
3.8%
u4778
 
3.7%
Other values (33)50792
39.3%

antiquity
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.69675
Minimum0
Maximum13
Zeros447
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-05-29T17:40:23.396850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median7
Q310
95-th percentile13
Maximum13
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.853197606
Coefficient of variation (CV)0.5753832242
Kurtosis-1.190904861
Mean6.69675
Median Absolute Deviation (MAD)3
Skewness0.02676276959
Sum133935
Variance14.84713179
MonotonicityNot monotonic
2021-05-29T17:40:23.480479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
91823
9.1%
11759
8.8%
31670
 
8.3%
81597
 
8.0%
41577
 
7.9%
131498
 
7.5%
51463
 
7.3%
121447
 
7.2%
71445
 
7.2%
101421
 
7.1%
Other values (4)4300
21.5%
ValueCountFrequency (%)
0447
 
2.2%
11759
8.8%
21414
7.1%
31670
8.3%
41577
7.9%
51463
7.3%
61292
6.5%
71445
7.2%
81597
8.0%
91823
9.1%
ValueCountFrequency (%)
131498
7.5%
121447
7.2%
111147
5.7%
101421
7.1%
91823
9.1%
81597
8.0%
71445
7.2%
61292
6.5%
51463
7.3%
41577
7.9%

Interactions

2021-05-29T17:35:09.164301image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:35:54.652742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:36:19.196472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:36:43.000255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:36:49.115514image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:36:58.055465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:37:22.290868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:37:43.777271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:38:13.122547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:38:36.163715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:38:36.257717image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:38:36.353260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:38:36.444549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:38:36.539315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:38:36.628713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:38:36.724945image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:38:36.860974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:38:55.331300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:38:55.417727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:38:55.503278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:38:55.592461image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:38:55.682666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:38:55.778948image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:38:55.877612image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:38:55.983343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:01.082658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:01.167885image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:01.257547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:01.344710image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:01.436111image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:01.534715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:01.628894image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:01.731491image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:06.871193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:06.968329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:07.057209image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:07.149031image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:07.240162image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:07.336206image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:07.426323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:07.557509image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:32.721943image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:32.813124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:32.904806image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:32.998487image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:33.094808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:33.189943image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:33.283118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:33.413747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:50.894778image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:50.992809image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:51.084853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:51.175155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:51.265621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:51.363514image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:51.459197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:39:51.594726image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:40:19.245147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:40:19.344205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:40:19.440447image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:40:19.534361image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:40:19.627887image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:40:19.721965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-29T17:40:19.816622image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-05-29T17:40:23.577545image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-29T17:40:23.697942image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-29T17:40:23.812807image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-29T17:40:23.936353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-29T17:40:24.069857image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-29T17:40:20.033158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-29T17:40:20.267813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-05-29T17:40:20.459535image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-05-29T17:40:20.545219image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

car_idcar_typecolorkmaverage_pricetransmissionbody_typeDIpricesolddiff_pricediff_price_perccard_brandantiquity
00SupraBlue25848512194799.0ManualSedanNaNNaNNaN-1.010101e+09-1.010101e+09Toyota1
1112SupraBlack25913421194799.0ManualSedanNaNNaNNaN-1.010101e+09-1.010101e+09Toyota1
21497SupraBlack24069281194799.0ManualSedanNaNNaNNaN-1.010101e+09-1.010101e+09Toyota1
31891SupraBlue21828354194799.0ManualSedan27.0211287.0True-1.648800e+04-8.464109e-02Toyota1
41955SupraBlack25620849194799.0ManualSedanNaNNaNNaN-1.010101e+09-1.010101e+09Toyota1
52435SupraBlue19606492194799.0ManualSedanNaNNaNNaN-1.010101e+09-1.010101e+09Toyota1
62645SupraWhite21322640194799.0ManualSedan12.0216338.0False-2.153900e+04-1.105704e-01Toyota1
73640SupraBlack27858456194799.0ManualSedanNaNNaNNaN-1.010101e+09-1.010101e+09Toyota1
83743SupraRed20518362194799.0ManualSedan13.0212860.0True-1.806100e+04-9.271608e-02Toyota1
94405SupraWhite24583516194799.0ManualSedanNaNNaNNaN-1.010101e+09-1.010101e+09Toyota1

Last rows

car_idcar_typecolorkmaverage_pricetransmissionbody_typeDIpricesolddiff_pricediff_price_perccard_brandantiquity
1999014019S80Blue12582104142081.0AutomaticSUVNaNNaNNaN-1.010101e+09-1.010101e+09Volvo7
1999115396S80Red18468745142081.0AutomaticSUVNaNNaNNaN-1.010101e+09-1.010101e+09Volvo7
1999215629S80Blue15260331142081.0AutomaticSUVNaNNaNNaN-1.010101e+09-1.010101e+09Volvo7
1999315855S80White14868440142081.0AutomaticSUVNaNNaNNaN-1.010101e+09-1.010101e+09Volvo7
1999416169S80Black14111995142081.0AutomaticSUVNaNNaNNaN-1.010101e+09-1.010101e+09Volvo7
1999516468S80Black14232987142081.0AutomaticSUVNaNNaNNaN-1.010101e+09-1.010101e+09Volvo7
1999616971S80Black18144875142081.0AutomaticSUVNaNNaNNaN-1.010101e+09-1.010101e+09Volvo7
1999717013S80Red16447711142081.0AutomaticSUVNaNNaNNaN-1.010101e+09-1.010101e+09Volvo7
1999818235S80Red11665859142081.0AutomaticSUVNaNNaNNaN-1.010101e+09-1.010101e+09Volvo7
1999918543S80Black18127179142081.0AutomaticSUVNaNNaNNaN-1.010101e+09-1.010101e+09Volvo7